资源环境科技发展态势分析平台

Saturated hydraulic conductivity (K-s) is among the important soil properties that influence the partitioning of rainfall into surface and subsurface waters. Point estimates of K-s are difficult to determine and exhibit large spatial variability in fields. Often, data from field-scale rainfall-runoff experiments are utilized to assess the properties of the K-s random field that are required in the use of field-scale infiltration models. Standard methods of calibration are confounded by nonuniqueness and identifiability problems associated with experimental data. In this study, a new method that employed a field-averaged infiltration model and Monte Carlo simulations was used to obtain the possible range of distributions of K-s that would describe experimental observations over a field for a rainfall event. A Shannon information-theoretic approach was subsequently adopted to consolidate the ranges of K-s distributions over multiple rainfall events to yield the best range of K-s distributions. The method was applied to data from several rainfall-runoff events observed under natural conditions over an experimental field characterized by a silty loam soil and a small surface slope. Results suggest the existence of numerous parameter combinations that could satisfy the experimental observations over a single rainfall event, and high variability of these combinations among different events, thereby providing insights regarding the identifiable space of K-s distributions from individual rainfall experiments. Validation results showed that the method provides a realistic estimate of our ability to quantify the spatial variability of K-s in natural fields from rainfall-runoff experiments.